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Despite their potential, AI detectors often fall short of accurately identifying and mitigating cheating.

In the age of advanced artificial intelligence, the fight against cheating, plagiarism and misinformation has taken a curious turn.

As developers and companies race to create AI detectors capable of identifying content written by other AIs, a new study from Stanford scholars reveals a disheartening truth: these detectors are far from reliable. Students would probably love to hear this.

Eye drops that have been contaminated with a highly drug-resistant strain of bacteria have now been linked to four deaths and 14 cases of blindness, according to an update from the Centers for Disease Control and Prevention (CDC).

The CDC said in a post on its website that it has identified 81 patients from 18 states who have been infected with VIM-GES-CRPA, a rare strain of a drug-resistant bacterium called P. aeruginosa. This includes 13 additional patients from the agency’s previous update in March.

Researchers at Stanford University have developed digital skin that can convert sensations such as heat and pressure to electrical signals that can be read by electrodes implanted in the human brain.

Although such capability was developed years earlier, the components required at that time to convert digital signals were rigid and unwieldy.

This new is soft as, well, skin. The conversion elements are seamlessly incorporated within the skin, which measures a few tens of nanometers thick.

Alzheimer’s disease (AD) is a complex neurodegenerative illness with genetic and environmental origins. Females experience faster cognitive decline and cerebral atrophy than males, while males have greater mortality rates. Using a new machine-learning method they developed called “Evolutionary Action Machine Learning (EAML),” researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital have discovered sex-specific genes and molecular pathways that contribute to the development and progression of this condition. The study was published in Nature Communications.

“We have developed a unique machine-learning software that uses an advanced computational predictive metric called the evolutionary action (EA) score as a feature to identify that influence AD risk separately in males and females,” Dr. Olivier Lichtarge, MD, Ph.D., professor of biochemistry and at Baylor College of Medicine, said. “This approach lets us exploit a massive amount of evolutionary data efficiently, so we can now probe with greater accuracy smaller cohorts and identify involved in in AD.”

EAML is an ensemble computational approach that includes nine machine learning algorithms to analyze the functional impact of non-synonymous coding variants, defined as DNA mutations that affect the structure and function of the resulting protein, and estimates their deleterious effect on using the evolutionary action (EA) score.

Our technological age is witnessing a breakthrough that has existential implications and risks. The innovative behemoth, ChatGPT, created by OpenAI, is ushering us inexorably into an AI economy where machines can spin human-like text, spark deep conversations and unleash unparalleled potential. However, this bold new frontier has its challenges. Security, privacy, data ownership and ethical considerations are complex issues that we must address, as they are no longer just hypothetical but a reality knocking at our door.

The G7, composed of the world’s seven most advanced economies, has recognized the urgency of addressing the impact of AI.


To understand how countries may approach AI, we need to examine a few critical aspects.

Clear regulations and guidelines for generative AI: To ensure the responsible and safe use of generative AI, it’s crucial to have a comprehensive regulatory framework that covers privacy, security and ethics. This framework will provide clear guidance for both developers and users of AI technology.